Validating the results of a route choice simulator Transportation Research C 5

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... This would lead to a combinatorial problem for any methodology. Fortunately, it is common for three to four paths to carry the vast majority of transport O/D flow, and very rarely are more than six or seven routes utilized (Bonsall et al. 1997;Hazelton 2000). So, H I has the maximum limit of paths ðψÞ ¼ 7. ...
Traffic flow data are needed for traffic management and control applications as well as for transportation planning issues. Such data are usually collected from traffic sensors; however, it is not practical or even feasible to deploy traffic sensors on all of a network's links. Instead, it is necessary to extend the information acquired from a subset of link flows to estimate the entire network's traffic flow. To this end, this study proposes a robust deep learning architecture based on a stacked sparse autoencoders (SAEs) model for a precise estimation of the whole network's traffic flow with an already-deployed sensor set. The proposed deep learning architecture has two consequent components: a deep learning model based on the SAEs and a fully connected layer. First, the SAEs model is used to extract traffic flow features and reach a meaningful pattern of the relation between the traffic flow data and network structure. Subsequently, the fully connected layer is used for the traffic flow estimation. Then, the whole architecture is fine-tuned to update its parameters in order to enhance the traffic flow estimation. For training the proposed deep learning architecture, synthetic link flow data are randomly generated from the network's prior demand information. The performance of the proposed model is evaluated then validated using two real networks. A third medium real-size network is used to measure the robustness of applying the proposed methodology to this specific problem of traffic flow estimation.
... IGOR (Interactive Guidance on Routes) by Bonsall and Parry [9], is the first simulator introduced, which simulates en-route travel through a network and the provision of real-time information. Following IGOR_s path is VLADIMIR by Bonsall et al. [10], which has as primary purpose to explore the influence of route-guidance and information on drivers_ route choice. The next simulator, developed at MIT by Koutsopoulos et al. [34], is used to collect data and calibrate a route-choice model incorporating concepts of fuzzy logic and fuzzy set theory. ...
A series of travel simulators have been developed in the past two decades under the Intelligent Transportation Systems (ITS) umbrella. They have addressed issues such as reactions to advisory radio and variable message signs, use of navigation systems, route diversion, and mode choice. The objective of this paper is to present the design and implementation of a different kind of travel simulator. GABRIEL (Gis Activity-Based tRavel sImuLator) has as a foundation the activity-based approach and makes use of geographic information systems (GIS) as a development environment. The simulation scenario consists of a commute trip where two activities take place. En-route to the first destination, congestion occurs and subjects are requested to take action based on a set of alternatives. The simulator provides re-routing, destination substitution, dynamic geographic information and real-time information to aid users in their decision-making process. As a result it helps subjects in developing their ability to adapt given a particular scenario and allow researchers in understanding trip making, activity rescheduling, and the decision-making process from a comprehensive perspective.
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